Minimizing an objective function is of central importance in machine learning. In this problem, we will analyze the an iterative approach for finding B e Rd that approximately) minimizes || AB 6||3 for a given matrix A Rnxd and vector b E R. Consider the following iteration approximation algorithm: Initially, B(0) = (0,...,0) R' is the zero vector in Rd. . For k = 1,2, ...,N: - Compute B(k) := B(k-1) + nAT(6 - AB(k-1)). In above, n > 0 is a fixed positive number usually referred to as the step size, and N is the total number of iterations. Define M := AT A and v := AT6. (1) Show that the matrix M is symmetric positive semi-definite. Throughout, assume that the eigenvalues of M, denoted by 11,..., dd, satisfy l; 0 is a fixed positive number usually referred to as the step size, and N is the total number of iterations. Define M := AT A and v := AT6. (1) Show that the matrix M is symmetric positive semi-definite. Throughout, assume that the eigenvalues of M, denoted by 11,..., dd, satisfy l;